Journal of Critical Reviews COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING MANET AND VANET INFRASTRUCTURE

Abstract

MANET and VANET are emerging technology in the current trend for research. VANETs are a subclass of MANETs. In MANET, nodes are connected by wireless channels in-network and each node acts as a router and as host. One of the scenarios of MANET is Vehicular ad-hoc networks (VANET). For communication in VANET, the vehicles interacting between themselves as well as along with roadside device stations, efficient routing Protocols are needed. This paper represents the performance of ACO (Ant colony optimization) and PSO (Particle Swarm Optimization) in MANET as well as VANET for efficiently transmit the data in the shortest route to reach the destination and also evaluates energy consumption and load balancing among MANET and VANET.

Full text

Journal of critical reviews 1197
Journal of Critical Reviews
ISSN- 2394-5125 Vol 7 , Issue 9, 2020
COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND
LOAD BALANCING IN EMERGING MANET AND VANET INFRASTRUCTURE
S.Murugan1, S.Jeyalaksshmi2,B.Mahalakshmi3,G.Suseendran4,T.Nusrat Jabeen5,R.Manikandan 6
1Assistant Professor, Department of CSE, Sri Aravindar Engineering College, Tamil Nadu,India
Email Id: smartrugans@gmail.com
2 Assistant Professor, Department of Information Technology, Vels Institute of Science, Technology & Advanced
Studies (VISTAS), Chennai, India
Email : pravija.lakshmi@gmail.com
3Assistant Professor, Department of Information Technology, Vels Institute of Science, Technology & Advanced
Studies (VISTAS), Chennai, India
Email: maha.karthik921@gmail.com
4Assistant Professor, Department of Information Technology, Vels Institute of Science, Technology & Advanced
Studies (VISTAS), Chennai, India
Email : suseendar_1234@yahoo.co.in
5Assistant Professor, Department of Computer Science, Anna Adarsh College for Women, Chennai, India
Email : tn_jabeen@yahoo.co.in
6Assistant professor, School of Computing, SASTRA Deemed University,Thanjavur, India.
Email : srmanimt75@gmail.com
Received: 24.03.2020 Revised: 25.04.2020 Accepted: 26.05.2020
Abstract
MANET and VANET are emerging technology in the current trend for research. VANETs are a subclass of MANETs. In MANET, nodes are
connected by wireless channels in-network and each node acts as a router and as host. One of the scenarios of MANET is Vehicular ad-
hoc networks (VANET). For communication in VANET, the vehicles interacting between themselves as well as along with roadside device
stations, efficient routing Protocols are needed. This paper represents the performance of ACO (Ant colony optimization) and PSO
(Particle Swarm Optimization) in MANET as well as VANET for efficiently transmit the data in the shortest route to reach the destination
and also evaluates energy consumption and load balancing among MANET and VANET.
Keywords: ACO, PSO, VANET, MANET.
© 2020 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
DOI: http://dx.doi.org/10.31838/jcr.07.09.219
INTRODUCTION
In many applications, WSN is utilized. They have several
limitations such as less energy, communication and computation
ability. While designing protocols for WSNs these limitations are
considered. Due to these limitations, many routing methods like
MANETs and end-to-end devices are inappropriate [1]. MANET is
infrastructure less mobile network which has two or more nodes
equipped with network capacity, wireless communication
without network control.
VANETs has a challenging class of MANETs. VANETs are usually
distributed and self-organizing communication network made up
of dynamic vehicles. It has very high node mobility as well as less
degree of freedom in mobility patterns. For MANETs they are a
number of routing protocols [2] but this paper deals with VANET
with Adhoc routing protocols which is used in unpredictable
conditions.
MANET has more importance in military, commercial, private
and public sectors because of increased use of handheld wireless
devices like cell phones, tablet, computers, PDAs, and so on.
Openness, as well as the flexibility of MANET, makes attractive
for different types of applications like emergency search as well
as rescue operations, military communication, firefighting,
disaster recovery and so on. [3] It WLAN, which has no
centralized structure like access points or base stations. To give
proper communication between 2 mobile nodes in MANETs
direct transmission range. In multi-hop fashion, intermediate
nodes are utilized to forward packets. Consider all ad hoc routing
protocols in mobile nodes has cooperative, trustworthy, reliable
in the network [4]. Without any infrastructure, it can data are
transmitted from sender to receiver. Any node can work as a
router which receives as well as send packets [5]. By using the
clustering method, route traffic delay is reduced as well as data
transfer control is enhanced when depends on the choice of
routing protocol type. To obtain optimization and reinforce
Quality of Service (QoS) is the main objective of the smart city in
urban resources. Using several factors such as energy
consumption, throughput, packet loss and end-to-end delay, QoS
is calculated [6].
Related Work
R Manikandan et al., (2019) [7] presents energy-efficient load
balancing (EELB) technique for changing load handling and
energy efficiency advantages. By measuring the battery power of
nodes, it can handle various traffic rates to achieve non-
overloading energy constraints as well as seamless
communication. To ensure prolonged link availability, for
distribution of traffic flows on residual energy as well as nodes
capacity, it manages network communication. Using MATLAB,
the performance of this method is calculated by using
parameters like the first node dies time, the signal at BS and
Throughput.
Osamah I Khalaf et al., (2015) [8] proposed performance analysis
of 2 significant reactive routing protocols like Dynamic Source
Routing (DSR) as well as Ad-Hoc on Demand Distance Vector
Routing (AODV). In terms of route discovery time, end-to-end
delay, number of hopper route are compared. DSR maintains low
overhead even in presence high mobility rate.

COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING
MANET AND VANET INFRASTRUCTURE
Journal of critical reviews 1198
Stutzle and Holger H. (2011) [9] presents the Min-Max Ant
System (MMAS) in MANET. By means of an experimental study in
several important aspects, this novel represents how MMAS
differs from Ant system. Moreover, this novel relates MMAS
characteristics by using a greedier search instead of Ant system,
which results in search space analysis. The main limitation of this
research is coverage issue and combinatorial optimization.
Nasab et al. (2012) [10] presents multicast routing based on
particle swarm optimization (MPSO), which focuses on delay and
energy-efficient in multicast routing in MANET. For route
selection, it chooses node with less energy consumption and
creates a multicast tree with less delay.
Hossein et al., (2013)[11] proposed a proactive method known
as Ant routing technique for mobile ad hoc networks (ARAMA).
To collect path data is the main task of forwarding ant in other
ACO techniques. For managing forward ant’s generation rate, this
research suggests that routing discovery and maintenance are
drastically reduced. This novel has the limitation that it does
analyze how to manage the generation rate in a dynamic
environment.
Lin & Deng (2015)[12] proposed PSO method for hybrid VANET
sensor network for two-lane placement issue. There exist two
methods to solve this issue. First one is the Integer Linear
Program (ILP), and the second one is Center PSO method which
is theoretical analysis. For moderate problems, these methods
perform well[13]. Future work contains heterogeneity,
constraints and other objective functions. This novel has the
limitation that cross-layer design of the hybrid network.
Proposed Method
Because of resource constraints and dynamic behaviour in
MANETs, traditional routing protocol face many issues. The
biologically inspired mechanism is utilized to overcome this
issue. For colony survival of each individual, the social
organization of ant is genetically evolved, which is key factor for
their success[14]. This insect organization shows the fascinating
property in which society and individual activity are not
regulated by centralized control explicit form. The most popular
and successful research in ant algorithm related to combinatorial
optimization issues which comes under metaheuristic Ant Colony
Optimization (ACO).
For communicating roadside and vehicle framework, board
safety system called VANET is utilized. Every node in VANET can
communicate with others and quickly move within the network
[15].ACO algorithm is to design that uses the nodes position and
the speed information available in the vehicular network. By
using the source as well as destination cluster head ants always
traverse the shortest path from source to destination. For
routing network, the shortest path is selected.
ACO ALGORITHM
In graph to discover the shortest path between source and
destination, ant colony techniques are utilized, which stimulates
ant’s searching behaviour and applies to different practical
issues. It creates the shortest path between nest to a food source
to search for food. ACO is an algorithm that frames solution
based on problem data and gives application to discrete
optimization issue. In an unexpected way, Ant searches for a food
source. They take food to their colony when it finds a food
source. Pheromone is a chemical substance, which is played by
Ant along paths where they travel. Moreover, the shortest paths
have more pheromone trails which are used as a communication
mechanism between ants. Based on solution quality, pheromone
trail strength is deposited on the ground. Multiple ants in shorter
paths have high pheromone trails which cause higher density
that makes more attraction when compared to longer paths. At
evaporation rate, pheromone trails are reduced. In local
minimum evaporation process gives exploration and prevents
stalling. At the end of each iteration, pheromone values are
updated.
(1)
Where,

is probability, when ant k selects to shift from node i to node j.
This decision based on pheromone level as well as heuristic data.
is feasible neighbourhoods set which does not visit by ant k,
ŋij is a heuristic function,
τij is the amount of pheromone on edge i and j, α and β are
specifications that finds the relationship between heuristic data
and pheromone concentration. Formation of pheromone update
is represented as follows:
τij← τij + Δ
 (2)
Δ
=

 (3)
The equation of evaporation update is given by:
  (4)
where,
 is a constant factor reduction of all pheromones,
f() is the cost of the solution by ant k, and Q is constant.
After a certain amount of iteration, the above optimization
method is terminated.
PSO ALGORITHM
Multi-agent parallel search method called particle swarm
optimization (PSO) in which each particle shows a potential
solution in a swarm and maintains swarm of particles. Based on
its own experience of neighbours, all particles fly through
multidimensional search space by adjusting its position. It has
two operators—position and velocity update. In every
generation, every particle is accelerated from particles previous
to the global best position.
Xid={xi1,xi2,xi3,……xin} presenting a position in m-dimension space,
with the particle i.
Xid(t+1) =Xid + Vi (t+1) presenting the next position.
Xid+1:modified position.
      (5)
   (6)
Vid is particle velocity,
C1 : cognitive acceleration coefficient,
w : inertia weight,
Pgd(t) : global best position in group of particles,
C2 : social acceleration coefficient,
r1,r2: uniformly distributed random numbers in range
[0 to 1],
Pid : particles own best position,
Xid is particles current position,
Updating next global best position
Pgd(t+1) =
   (7)
Gbest= min {pgd(t+1,i)} where I € {1,2,3…..n}
otherwise 0
allowed j if
][)]([
][)]([
)( k







=
k
allowedk ikik
ijij
k
ij t
t
tp





COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING
MANET AND VANET INFRASTRUCTURE
Journal of critical reviews 1199
For updating particles velocity, velocity components are critical.
It consists of three terms. They are,
1. Term vij(t) denotes inertia c which gives previous
flight direction memory or immediate past movement.
2. C1r1(Pgd - Xid] is known as a cognitive component that
calculates particles performance relative to past.
3. C2r2[Gbest-Xid] is called a social component that
measures particles performance relative to particles
or neighbours group.
With a group of nodes, PSO is initialized in MANET, which
searches for optimal node solution by updating generations.
According to the specified number of nodes it is given by
randomly generating population. For achieving uniqueness, each
selected node has entire node IDs which does not repeat in terms
of ID.
In iterations, each node has 2 best values. First is the best
solution which is previously obtained. Second is found by particle
swarm optimizer produced by any node in population. During a
particular period until the current iteration, when node achieves
the best fitness which is recorded as Pid called particle best.
During every particular iteration in population, which has its
overall best output is known as Gbest. Inertial range option
bound is utilized for giving satisfactory solution which is found.
This value is called the global best value.
LOAD BALANCING AMONG NODE
Load balancing is an important solution to enhance better energy
management and implementation time by minimizing load
imbalances. Cooperative approach for load balancing among the
network which is used for the node having more than one
incoming request that time traffic or load high in that particular
node. By using a cooperative approach, it shares their work by its
neighbour node. Network load is increased result work done is
decreased, so we reduce the traffic among the network while
transferring the data.
ENERGY CONSUMPTION
For processing, each nodes uses some amount of energy is called
Energy consumption. While consuming energy Kbit of
information receives i sensing element is represented as Elec* k
(1) indicates energy consumption while sending information in
the packet to element j represented by eqn.
 
(8)
Where dij is the weight between nodes i and j
Eqn 2 gives energy transmission in one bit
  
(9)
Where,
ptd - power utilized for transmitting nodes concerning distance
pd1- power dissipate during 1 bit data send
PERFORMANCE ANALYSIS
The performance analysis of the paper is simulated in NS2
environment with the following figure. The parameters used are
Successive transmission, packet delivery ratio, delay, packet
drop, dropping ratio, throughput and Goodput.
Table 1 Performance analysis compared with MANET and VANET
Parameter
VANET
MANET
Number of nodes
50
50
Initial energy of node
100 J
100 J
Simulation time
150 ms
100 J
Energy consumption
22 %
27 %
Dropping ratio
7 %
9.8 %
Packet delivery ratio
94.42 %
94.22 %
Throughput
67.675 %
69.456 %
End to end delay
0.333 ms
0.453 ms
Load in each node
2.5 mbps
2.5 mbps

COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING
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Journal of critical reviews 1200
Figure. 1: Graph for successive transmission
Figure 1 shows the successive transmission is the intensity of the successful transmission of a packet in the network. It can be measured
across time by the above algorithm which is simulated in the environment
Figure.2: Graph for packet delivery ratio(PDR)
Figure 2 shows PDR, which is a number of data packets delivered successfully to generated data packets by source. To calculate data
packets delivery ratio, it trace files which are post-processed that shows the relation between sent and received packets.

COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING
MANET AND VANET INFRASTRUCTURE
Journal of critical reviews 1201
Figure.3: Graph For Delay
Figure 3 show delay was predictable because of ACO and PSO takes the shortest way to reach the destination.
Figure.4: Graph For Throughput
Figure-4 represents the throughput obtained by the above
algorithm concerning time. It varies for different algorithms.
When compared to other methods, this method achieves high
throughput. In the communication channel, throughput is the
average rate of message delivery. This data is delivered over the
logical or physical link or pass through some network node. It is
calculated as bits per second (bit/s or bps), sometimes in per
second.

COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING
MANET AND VANET INFRASTRUCTURE
Journal of critical reviews 1202
Figure.5: Graph For Goodput
Figure 5 shows the Goodput, i.e. number of bits received in the network at some destination per unit time. Amount of data is taken as
protocol overhead and retransmitted data packets. Amount of time is considered as first packets first bit until the last bit of the last
packet delivered.
Figure.6: Graph for drop
Figure-6 shows packet drop analysis obtained by using ACO technique concerning time. Overall efficiency is increased by using this
technique. This shows less delay time, and high efficiency is easily achieved.

COMPARISON OF ACO AND PSO ALGORITHM USING ENERGY CONSUMPTION AND LOAD BALANCING IN EMERGING
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Journal of critical reviews 1203
Figure.7: Graph for dropping ratio
Figure 7 shows the Packet drop ratio, which is calculated as subtraction of data packets sent from data packets received at the
application layer.
CONCLUSION
In VANET and MANET, the communication link is hazardous due
to disconnection. Particle swarm optimization (PSO) and Ant
Colony Optimization (ACO) are simulated by using specifications
like PDR, delay, throughput, Goodput, packet drop and dropping
ratio. With sudden improve in vehicular traffic in urban areas,
effective use of available resources is essential to reduce load as
well as energy consumption. The drop will be minimized by
reducing the traffic among the network and also balanced the
load. Network lifetime is increased by reducing energy
consumption in which it is necessary to balance energy in nodes.
By using ACO and PSO is evaluated that selects the most reliable
path which helps to reduce the possibility of link breakages, i.e.
particular zone area as well as responds better to changes in the
network topology.
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